LLM-based MOFs Synthesis Condition Extraction using Few-Shot Demonstrations
This addresses the challenge of automating MOFs synthesis extraction for materials science researchers, though it is incremental as it builds on existing LLM capabilities with few-shot learning.
The researchers tackled the problem of extracting Metal-Organic Frameworks (MOFs) synthesis conditions from literature by introducing a few-shot LLM in-context learning method, which outperformed zero-shot LLM and baselines, achieving over 91.1% high-quality MOFs in lab synthesis for specific surface area.
The extraction of Metal-Organic Frameworks (MOFs) synthesis route from literature has been crucial for the logical MOFs design with desirable functionality. The recent advent of large language models (LLMs) provides disruptively new solution to this long-standing problem. While the latest researches mostly stick to primitive zero-shot LLMs lacking specialized material knowledge, we introduce in this work the few-shot LLM in-context learning paradigm. First, a human-AI interactive data curation approach is proposed to secure high-quality demonstrations. Second, an information retrieval algorithm is applied to pick and quantify few-shot demonstrations for each extraction. Over three datasets randomly sampled from nearly 90,000 well-defined MOFs, we conduct triple evaluations to validate our method. The synthesis extraction, structure inference, and material design performance of the proposed few-shot LLMs all significantly outplay zero-shot LLM and baseline methods. The lab-synthesized material guided by LLM surpasses 91.1% high-quality MOFs of the same class reported in the literature, on the key physical property of specific surface area.